Behavior prediction based on daily-life record database in distributed sensing space

This paper proposes a behavior prediction system for supporting our daily lives. The behaviors in daily-life are recorded in an environment with embedded sensors, and the prediction system learns the characteristic patterns that would be followed by the behaviors to be predicted. In this research, the authors applied a method of discovering time-series association rules, which discovers frequent combinations of events called episodes. The prediction system observes behaviors with the sensors and outputs the prediction of the future behaviors based on the rules.